Latent class analysis of the Epidemic‐Pandemic Impacts Inventory on mental health outcomes in Siyan Clinical patients

Abstract Background and Aims The COVID‐19 pandemic has made an outsized negative impact on mental health worldwide. However, research indicates that this impact was not uniform. This study aimed to determine how mental health patients experienced the COVID‐19 pandemic to characterize mental health disparities and identify underlying factors. Methods We used the Epidemic‐Pandemic Impacts Inventory (EPII) and latent class analysis to determine the impacts of epidemics and pandemics across several life domains in 245 survey respondents, all of whom were mental health patients at Siyan Clinical. Respondents were predominately White (84.5%) and female (76.3%), with the majority being diagnosed with anxiety or mood disorders (76.3%). Results In the work life domain, respondents in the higher‐impact class were more likely to be employed and/or working in healthcare. In both the home life and emotional/physical health and infection domain, respondents with mood disorders, substance use disorders, or children under 18 living at home were more likely to be in the higher‐impact class. In the home life and positive change domains, respondents that were married were more likely to be in the higher‐impact class, indicating that this group experiences more impacts from the pandemic, both positive and negative. Finally, some groups stood out as having fewer impacts from the pandemic: respondents that were male, over age 55, White, and/or have anxiety disorders were more likely to experience fewer impacts from the pandemic in the work life and home life domains. Conclusions This study provides evidence that certain groups may experience greater or fewer impacts from the pandemic.


| INTRODUCTION
The COVID-19 pandemic has affected all our lives, particularly our mental health. 1 However, some subgroups or populations may be more at risk for negative mental health outcomes compared to others. It is important to recognize these at-risk groups so that they can be targeted by mental health interventional services. The COVID-19 pandemic has provided researchers and physicians with a unique opportunity to identify the groups most at-risk during times of extreme stress and heightened anxiety for adverse mental health outcomes.
In a recent study of mental health patients at Siyan Clinical, we used the Epidemic-Pandemic Impacts Inventory (EPII) survey recently developed by Grasso and colleagues 2,3 and found that the age groups least affected by COVID-19 included individuals aged 65 years and older. 4 This observation is in line with other research that has also noted older age may buffer against the negative impact of the COVID-19 pandemic on mental health. 5 Similarly, another study found that younger people self-reported more negative mental health outcomes during the COVID-19 pandemic. 4,6 Perceived social support may also buffer against the negative effects of the COVID-19 pandemic. 6,7 We also observed that people with children under the age of 18 years also reported more positive indicators associated with the pandemic compared to those without children at home, although it is worth noting that this same group also noted more negative indicators on the EPII survey. 4 We further found that genderqueer, nonconforming, and transgender individuals may also be at higher risk for more negative impacts associated with the pandemic. 4 This observation, too, is in line with other recent research focused on transgender individuals, as it was reported that transgender individuals in India were at increased emotional and social risks during the pandemic. 8 Without a doubt, more inclusive research is needed to understand the unique challenges populations such as these face. 9 In addition to these aforementioned groups (younger individuals, people with limited social support, and transgender/gender nonconforming individuals), it is likely that there are other subpopulations that are at an increased risk for negative effects associated with large-scale stressors such as the COVID-19 pandemic. Here, we sought to understand our prior study further by doing multivariate analyses on this data set with the goal of identifying those most atrisk during the ongoing pandemic.

| The EPII
We used the EPII tool as described previously. 4 The EPII is a newly

| Data and statistical analysis
Latent class analysis (LCA) is a statistical method to identify differences between subgroups that share certain characteristics.
LCA identifies subgroups (latent classes) by an observed response pattern to categorical indicator variables. Analysis was conducted using the poLCA package for R. 11 This statistical package uses expectation maximization and Newton-Raphson algorithms to obtain maximum likelihood estimates of model parameters. EPII items with an endorsement rate of less than 5% were excluded from LCAs, with 12 of the 92 questions (13.0%) omitted. Following a previous 2020 analysis by Grasso  For each domain, LCA models with the number of classes ranging from one to six were created. The final number of latent classes for each domain was identified by evaluating the Bayesian Information Criterion (BIC), with a lower BIC indicating better model fit. 12 Entropy was also examined as a diagnostic criterion, with values above 0.8 more desirable, although no definitive cutoff criterion exists. 13 After determining the best-fitting class solution for each domain, predicted class membership was compared to a set of dichotomous demographic characteristics using χ 2 test to identify differences between classes. A p < 0.05 was considered statistically significant. For domains with a best-fitting class solution of greater than two, post hoc χ 2 tests following the initial χ 2 were conducted with a Bonferroni adjustment (p < 0.02).

| Work
The work life LCA had a best-fitting model of two classes with 11 indicators (Supporting Information: Table 2). The two classes were split nearly evenly: Class 1 comprised 45.9% of the sample and Class 2 comprised 54.1% of the sample (see Table 3).  • Had to continue to work even though in close contact with people who might be infected (Class 1: 0.88, Class 2: 0.12).
• Spend a lot of time disinfecting at home due to close contact with people who might be infected at work (Class 1: 0.79, Class 2: 0.06).
• Increase in workload or work responsibilities (

| Home life
The home life LCA had a best-fitting model of three classes with 15 indicators (see Supporting Information: Table 3). For home life, the sample was split into three classes. About half (53.1%) of participants were grouped into Class 2, followed by 28.2% in Class 1, and 18.7% in Class 3 (see Table 3).
Class differentiation is visualized in Supporting Information: • Had to take over teaching or instructing a child (Class 1: 0.78, Class 2: 0.00, Class 3: 0.00).
When comparing the three classes on the home domain, several differences were noted (see Table 3 were more likely to be over age 55 and/or White. Respondents with moderate impacts (Class 3) were more likely to have reported a trauma disorder, eating disorder, and/or be under age 34.

| Social activities and isolation
The social activities and isolation LCA had a best-fitting model of three classes with 17 indicators (see Supporting Information: Table 4). Like home life, social activities, and isolation could be broken into three distinct classes. Class 3 had the largest percentage of respondents (38.4%), followed by Class 1 (35.1%) and Class 2 (26.4%; see Table 1).
Class differentiation is visualized in Supporting Information: • Unable to participate in social clubs, sports team, or usual volunteer activities (Class 1: 0.95, Class 2: 0.41, Class 3: 0.57).
When comparing the classes on social activities and isolation (Supporting Information: Table 4), we noted that Class 2 had a greater proportion of respondents than Classes 1 and 3 that identified as male, while Classes 2 and 3 had a greater proportion of respondents than Class 1 reporting substance abuse.
Overall, class differences were limited in the social activities and isolation domain. This may be due to generally high rates of item endorsement in this domain (see Supporting Information: Table 1 In short, respondents experiencing less impact (Class 2) in the social activities and isolation domain were more likely to identify as male. Respondents experiencing moderate impacts (Class 1) were more likely to not report substance abuse compared to people reporting substance abuse, who were more likely to experience less impact (Class 2) or greater impact (Class 3).

| Emotional/physical health and infection
The emotional/physical health and infection LCA had a best-fitting model of two classes with 18 indicators (see Supporting Information: Table 5). The two classes were mostly evenly split, with 56% of participants in Class 1 and 43.5% of participants in Class 2 (see Table 3).
Class differentiation is visualized in Supporting Information: When making class comparisons (Table 3), Class 2 (the class that experienced greater impacts) had a greater proportion of respondents than Class 1 with children under 18 living at home, report a trauma disorder, and/or report a mood disorder.

| Positive change
The positive change domain had a best-fitting model of two classes with 19 indicators (see Supporting Information: Table 6). The classes were mostly evenly split, with 58.6% of participants in Class 2 and 41.4% of participants in Class 1 (see Table 3).
Class differentiation is visualized in Supporting Information: • Paid more attention to preventing physical injuries (Class 1: 0.64, Class 2: 0.26).
Overall, a systematic comparison of the classes (Table 3) revealed that Class 1 (who experienced greater positive impacts according to the EPII survey) had a greater proportion of respondents than Class 2 who are married and/or report an anxiety disorder.

| DISCUSSION
Overall, we show here that certain groups may experience greater or fewer impacts from the pandemic depending on a given aspect of their lives. We found that employment (particularly employment in healthcare) was associated with higher impact from the pandemic.
Having children under the age of 18 was also associated with a higher impact. Being married was associated with both high negative impact and high positive change, interestingly. Males were less likely to be impacted on the social activities and isolation domain. Additionally, older individuals (aged 55 years and older) and White individuals were also less likely to report higher impacts from the COVID-19 pandemic. Altogether, this multivariate analysis adds greatly to our previously reported findings from this data set. These data also complement the growing body of literature on the impacts of COVID-19.
The results for our study comport with the growing consensus within the literature that identifies disparities in the effects of the COVID-19 pandemic on the mental health of specific groups. For example, our findings that older people were less impacted by the pandemic compared to their younger peers is in line with several reports showing that adolescents were particularly impacted by the pandemic. 14,15 Two studies of Italian teenage students found that students experienced significant sadness throughout the pandemic, with many citing the lack of an in-person community as a reason. 14,15 Interestingly, these studies and others found that male students experienced fewer symptoms than their female counterparts, a phenomenon highlighted in the current work. 14-16 Such gender differences were mirrored in our results, wherein male respondents were more likely to be sorted into Class 2 and experienced less severe effects on mental health from the pandemic. These results are in line with other reports finding that women bore the brunt of the pandemic in many cases due to increased caregiver activities and household chores, both of which were also observed in the current study. [16][17][18] Other studies indicate that gender-related disparities in mental health during the pandemic are also tied to domestic violence and unaccompanied birth, warranting future research. 19,20 Despite the relationship with marriage and domestic abuse during the pandemic, marriage is considered a protective factor in terms of mental health. 21 Another critical set of factors that influence COVID-19 pandemic-related mental health impacts is pre-existing mental illness.
Those living with mood disorders tended to suffer a greater impact from the pandemic, a result that mirrors previous research. 22 However, our data suggest that people living with anxiety before the pandemic were less affected. This finding necessitates further research, as other reports have noted a spike in anxiety due to the pandemic. 23,24 In addition, physical illness plays a role in the degree of impact an individual faced during the pandemic, particularly COVID-19 itself. 25 Both hospitalized and nonhospitalized patients display increased symptoms of posttraumatic stress disorder (PTSD), anxiety, and depression. However, increased symptoms were noted in people who were hospitalized due to the illness. 25 Understanding who is most at risk for impact by stressors such as pandemics is important because early intervention is key. TI081478-03).

CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT
Data will be made available upon request.

ETHICS STATEMENT
These procedures were reviewed by the Advarra IRB and conducted using ethical principles derived from international guidelines including the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guidelines.
Informed consent was obtained from all subjects involved in the study.

TRANSPARENCY STATEMENT
The lead author Anish Shah affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.